In this study, gallium oxide (Ga2O3) nanorods were deposited onto an indium tin oxide (ITO) glass substrate to develop a real-time living cell viability sensor. Ga2O3 nanorods had characteristics of cell population se...
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This work is focused on nanoscale viscoelastic properties characterization of gold nanoparticles (AuNPs) reinforced chitosan nanocomposites using nanoindentation. Chitosan nanocomposite films reinforced with gold nano...
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Wheelchair basketball for people with disabilities is a niche market with limited attention and resources due to its smaller scale. We utilize the characteristics of VR technology, combining wearable devices with phys...
Wheelchair basketball for people with disabilities is a niche market with limited attention and resources due to its smaller scale. We utilize the characteristics of VR technology, combining wearable devices with physical wheelchair seating, to create a virtual and real interactive game of wheelchair basketball. This enables people with disabilities to exercise at home and allows the general public to experience the challenges of disability sports, fostering empathy and emphasizing the importance of social justice.
Renal cell carcinoma (RCC) is a well-known malignancy characterized by specific gene mutations that elevate its occurrence. It can be categorized into clear cell RCC (ccRCC), papillary RCC, and chromophobe RCC, which ...
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This paper presents an intelligent optimization of the AI/ML framework to detect network anomalies via time collection analysis. The proposed framework entails leveraging an $\mathrm{AI} / \mathrm{ML}$ version to mann...
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ISBN:
(数字)9798350370249
ISBN:
(纸本)9798350370270
This paper presents an intelligent optimization of the AI/ML framework to detect network anomalies via time collection analysis. The proposed framework entails leveraging an $\mathrm{AI} / \mathrm{ML}$ version to manner the time series facts and to identify community anomalies. Traditional methods of describing the information through time variety or histogram analysis are then improved by introducing a supervised schooling set of rules for higher accuracy. The methodology is similarly validated using a real-global dataset. The result of the preliminary analysis famous that the $\mathrm{AI} / \mathrm{ML}$ model trained with the proposed framework enables efficient detection of extraordinary network sports. The findings of this examination underscore the benefits of using AI/ML models for the timely detection of community anomalies. The painting affords an intelligent optimization of AI/ML frameworks to locate community abnormalities via time collection evaluation. The proposed framework is evolved through leveraging a hard and fast of features, consisting of topology, via graph-primarily based strategies, an excellent way to seize the worldwide behavior of the community and trigger alarms in case of ability network anomalies. The time series evaluation is then achieved to extract and model features from the monitored traffic information to discover patterns and insights, also used in labeling and predicting a suitable elegance for each traffic file. In the end, the AI/ML model skilled the use of the categorized statistics to locate any ability community abnormalities and cause alarms to inform of such unusual behavior. The proposed framework, as a result, presents an automated solution for network anomaly detection and acts as a powerful tool in mitigating security threats for businesses and companies that rely heavily on their community infrastructure…
developments in network infrastructure and progress in sensor devices have brought the rise of the Internet of Everything (IoE). The technologies and applications are characterized by overall perception, tight connect...
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Background: This study investigates the potential of diffusion tensor imaging (dTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion–diffusion mismatch (PdM), utilizing a st...
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Background: This study investigates the potential of diffusion tensor imaging (dTI) in identifying penumbral volume (PV) compared to the standard gadolinium-required perfusion–diffusion mismatch (PdM), utilizing a stack-based ensemble machine learning (ML) approach with enhanced explainability. Methods: Sixteen male rats were subjected to middle cerebral artery occlusion. The penumbra was identified using PdM at 30 and 90 min after occlusion. We used 11 dTI-derived metrics and 14 distance-based features to train five voxel-wise ML models. The model predictions were integrated using stack-based ensemble techniques. ML-estimated and PdM-defined PVs were compared to evaluate model performance through volume similarity assessment, the Pearson correlation analysis, and Bland–Altman analysis. Feature importance was determined for explainability. Results: In the test rats, the ML-estimated median PV was 106.4 mL (interquartile range 44.6–157.3 mL), whereas the PdM-defined median PV was 102.0 mL (52.1–144.9 mL). These PVs had a volume similarity of 0.88 (0.79–0.96), a Pearson correlation coefficient of 0.93 (p < 0.001), and a Bland–Altman bias of 2.5 mL (2.4% of the mean PdM-defined PV), with 95% limits of agreement ranging from -44.9 to 49.9 mL. Among the features used for PV prediction, the mean diffusivity was the most important feature. Conclusions: Our study confirmed that PV can be estimated using dTI metrics with a stack-based ensemble ML approach, yielding results comparable to the volume defined by the standard PdM. The model explainability enhanced its clinical relevance. Human studies are warranted to validate our findings. Relevance statement: The proposeddTI-based ML model can estimate PV without the need for contrast agent administration, offering a valuable option for patients with kidney dysfunction. It also can serve as an alternative if perfusion map interpretation fails in the clinical setting. Key points: • Penumbral volume can be estimated by dTI combi
The performance of an electrochemiluminescence (ECL) immunosensor was improved with a particle gradient. SiO2-coated magnetic beads were adopted as nanocarriers for gradient manipulation and immobilized with the prima...
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This study explores the potential of employing arrays of FBG shock sensors to measure strain in high-G impact test systems. Through a meticulously designed experiment, we installed three FBG arrays into grooves on the...
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Tuberous sclerosis complex (TSC) is a genetic disease that causes benign tumors anddysfunctions in many organs, including the brain. Aside from the brain malformations, many individuals with TSC exhibit neuropsychiat...
Tuberous sclerosis complex (TSC) is a genetic disease that causes benign tumors anddysfunctions in many organs, including the brain. Aside from the brain malformations, many individuals with TSC exhibit neuropsychiatric symptoms. Among these symptoms, autism spectrum disorder (ASd) is one of the most common co-morbidities, affecting up to 60% of the population. Past neuroimaging studies strongly suggested that the impairments in brain connectivity contribute to ASd, whether or not TSC-related. Specifically, the tract-baseddiffusion tensor imaging (dTI) analysis provides information on the fiber integrity and has been used to study the neuropathological changes in the white matter of TSC patients with ASd symptoms. In our previous study, curcumin, a diet-derived mTOR inhibitor has been shown to effectively mitigate learning and memory deficits and anxiety-like behavior in Tsc2 +/− mice via inhibiting astroglial proliferation. Recently, gut microbiota, which is greatly influenced by the diet, has been considered to play an important role in regulating several components of the central nervous system, including glial functions. In this study, we showed that the abnormal social behavior in the Tsc2 +/− mice can be ameliorated by the dietary curcumin treatment. Second, using tract-baseddTI analysis, we found that the Tsc2 +/− mice exhibited altered fractional anisotropy, axial and radial diffusivities of axonal bundles connecting the prefrontal cortex, nucleus accumbens, hypothalamus, and amygdala, indicating a decreased brain network. Third, the dietary curcumin treatment improved the dTI metrics, in accordance with changes in the gut microbiota composition. At the bacterial phylum level, we showed that the abundances of Actinobacteria, Verrucomicrobia, and Tenericutes were significantly correlated with the dTI metrics FA, Ad, and Rd, respectively. Finally, we revealed that the expression of myelin-associated proteins, myelin bassic p
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